This paper presents an updated review of the external corrosion and failure mechanisms of
buried natural gas and oil pipelines. Various forms of external corrosion and failure …

Pipeline integrity and safety depend on the detection and prediction of stress corrosion
cracking (SCC) and other defects. In oil and gas pipeline systems, a variety of corrosion …

We articulate the design imperatives for machine learning based digital twins for nonlinear
dynamical systems, which can be used to monitor the “health” of the system and anticipate …

Nonlinear tracking control enabling a dynamical system to track a desired trajectory is
fundamental to robotics, serving a wide range of civil and defense applications. In control …

The ability of machine learning (ML) models to “extrapolate” to situations outside of the
range spanned by their training data is crucial for predicting the long-term behavior of non …

We develop and test machine learning techniques for successfully using past state time
series data and knowledge of a time-dependent system parameter to predict the evolution of …

In realistic systems of coupled oscillators, it is desired to predict the onset of synchronization
where the system equations are unknown, raising the need to develop a prediction …

In nonlinear dynamics, a parameter drift can lead to a sudden and complete cessation of the
oscillations of the state variables—the phenomenon of amplitude death. The underlying …

The rapid growth of research in exploiting machine learning to predict chaotic systems has
revived a recent interest in Hamiltonian neural networks (HNNs) with physical constraints …

Many natural and man-made systems are prone to critical transitions—abrupt and potentially
devastating changes in dynamics. Deep learning classifiers can provide an early warning …